This invention pertains to health, disability and life insurance systems, particularly including processing data (in the business of health insurance) for estimating future costs or liability and setting optimal pricing. For convenience, we call one embodiment of our invention More Accurate Predictions for Health Insurance Premiums or MAP4HIP.
Group health insurance is typically priced through a series of steps. Historical claims costs are calculated by summing the costs of insured individuals. Actuaries estimate what the general cost inflation trend will be next period. If an insured group is large enough to have credible experience (historical costs), the inflation trend may be applied to the historical claims experience to produce an estimate of the expected claims for next period. A profit margin and administrative costs are added to the expected group claims costs to produce the so-called “experience rate”. An underwriter reviews the group's experience and adjusts the cost and profit margin-based price depending on special circumstances and competitive pressure. The standard practice is to use group-level data for estimating costs and setting prices except for very small groups, individual policies or specific medical stop loss insurance. Information on the insured's (i.e., individual's) medical conditions is typically not used when group-level data are used for underwriting and pricing the group's aggregate cost forecast.
The current standard practice for estimating future health care costs for groups of 50 or more employees plus their dependents uses one of two methods or is a combination of those methods. If the group is large enough to have credible, stable experience, the historical costs are assumed to be the best estimate of next period's costs after a cost trend factor for inflation has been included. If the group is too small to have credible historical costs, many groups are combined together and averaged so that a stable demographic look-up table of historical average costs by age group by gender by family size can be developed and used as a weighting mechanism for estimating the expected future costs for non-credible groups. Cost trend factors for inflation are then applied. If a group does not have completely credible or non-credible experience, a blended average of its experience and a demographic look-up table forecast is used. These standard actuarial methods do not account for person-level trends in historical costs nor medical information about the person.
Small groups (i.e., 50 or fewer employees plus their dependents) or individual medical policies may use medical questionnaires from initial enrollment applications as input to an underwriter for estimating next period's group-level costs. Manual underwriting is expensive due to the labor intensity and is prone to variability among underwriters as their experience varies.
Some state Medicaid HMO programs (e.g., Colorado and Maryland) and federal Medicare HMO programs are using statistical algorithms that make person-level cost forecasts based on diagnoses from the computerized medical bills and demographic factors. These “risk adjustment” methods do not use procedures or historical person-level costs as the governments do not want incentives for increased utilization of services and spending more money. The governments' intent for HMO payments or managed care is to make payments proportional to the insured populations need for care based on their health conditions but not on prior care. However, historical cost is the single best predictor of future medical cost for credible groups. Not using it as part of the forecasting method decreases the accuracy of the forecast.
Some medical insurance companies may be using such “risk adjustment” algorithms used by Medicare, Medicaid and others intended for managed care cost forecasting or payment allocation. However, the prospective use of historical costs, types of services and procedures as well as diagnoses and demographics, as well as combinations of these variables, to produce more accurate cost forecasts than “risk adjustment” algorithms using only diagnoses and demographic factors, would be desirable.
There are person-level diagnosis and procedure models that measure the efficiency of medical practices (i.e., costs of care given the patient's conditions). These models are typically concurrent or retrospective in nature and not prospective. Symmetry's ETGs are a good example of this class of models. It lacks cost experience as a predictor since that is intended as the dependent variable. It also may limit use of demographic variables. Forecasting models would be desirable which are prospective and not designed for concurrent or retrospective analysis. The methods of the present invention can be applied to concurrent data to develop models for efficiency analysis, as will be described.
Stop loss health (or medical) insurance is typically purchased by self-insured employers that wish to limit their medical expense exposure. The most common form of medical stop loss insurance is known as “specific stop loss” insurance which is a high deductible (usually $25,000 to $100,000) insurance policy per insured person. Specific stop loss medical insurance is designed to protect the employer or other payer from large catastrophic medical expenses such as those incurred for liver transplants or care for neonates with major repairable congenital anomalies. The standard method for underwriting specific stop loss medical insurance uses a demographic look-up table to estimate costs for individuals whose medical expenses were under 50% of the deductible in the previous year. If an insured's medical expenses were over a predetermined amount, such as over 50% of the specific deductible, the insured's medical records are reviewed manually by an underwriter, and next year's costs are estimated by the underwriter or a doctor or nurse using their experience and expert opinion. Manual medical underwriting for specific stop loss has the same problems as manual underwriting for small group medical insurance; it is expensive and prone to underwriter variability.
Frequently, “aggregate stop loss medical insurance” coverage is also purchased by the employer. Aggregate coverage (exclusive of specific payments) means that the insurer will pay the employer's or other payer's medical cost obligations for a covered group if those costs exceed an agreed upon amount (i.e., an “attachment point”). The attachment point is typically defined as 125% of the group's expected cost in the insured period. The industry standard for calculating the expected cost is substantially the same method as used for fully insured plans. In other words, if the group is large enough to have completely credible experience, the last year's experience is modified by forecast inflation and increased by 25% to produce the 125% attachment point. If the group's experience is partially credible, then a weighted combination of experience and demographic look-up table model is used with an inflation forecast and increased 25% to calculate the 125% attachment point. When the group is too small to have credible experience, the demographic look-up table model is used as the starting point then trended inflation increased by 25% is used to calculate the 125% attachment point. Aggregate only medical stop loss insurance has been recently offered by one company (Cairnstone) to credible groups, and we believe that it uses group-level experience plus trended inflation to estimate future costs. Price is usually determined by competitive pressure but the inventors are not familiar with proprietary techniques used by the insurers.
We are including a glossary of terms that are used in describing the invention so that we are precise in our description. Additionally, SAS computer code and CART modeling language will be included to provide concrete examples of the implementation of the process or products. The software Appendix found on the compact disc filed with the present disclosure contains computer code (minus copyrighted formats) of a simpler embodiment of the invention. That code is in SAS and S Plus and the regression tree used is RPART. Details are provided for the fully insured renewal product. The aggregate only stop loss product uses the same steps for cost estimation. The short term disability, long term disability and life insurance products use the same techniques for forecasting but the dependent variables are changed to reflect the insurance type.